Publication Date

Sustainably Supporting Data Variability

A challenge in building complex, data-intensive systems is how to sustainably support data variation, schema, and feature evolution. Three speakers share experiences.

Publisher: Software Engineering Institute

This presentation was created for a conference series or symposium and does not necessarily reflect the positions and views of the Software Engineering Institute.

Abstract

A big challenge in building complex, data-intensive systems is how to
sustainably support data variation, schema, and feature evolution. In
this session, three speakers—Atzmon Hen-Tov, a senior architect of a
highly adaptable Telco platform; Jordan Menzin, architect of a Boston
Health Economics’ health-care analytics system; and Joseph Yoder of The
Refactory—share their experiences and hard-fought wisdom gained from
building complex, data-intensive systems.

Invited Talk: Data Upgrade as a First-Class Citizen

Atzmon Hen-Tov explains how the ModelTalk system addresses data upgrade
as an integral part of its product line architecture. Complex,
large-scale business support systems in the telecommunication industry
require high dependability while market pressures demand frequent
releases. One aspect that hampers agility in a highly dependable system
is data migration. In Pontis’ ModelTalk, an executable modeling
framework, upgrades are first-class citizens, allowing for rapid
evolution, agility, and reuse while at the same time supporting multiple
persistency technologies.

Invited Talk: High-Performance Dynamic Health-Care Analytics

Jordan Menzin of Boston Health Economics (BHE) reviews the core
architecture and key decisions that went into the creation of Instant
Health Data, a highly performant health-care analytics system.
Leveraging distributed computing and domain modeling, BHE has created an
extensible platform that enables researchers to complete analytics
projects using diverse data sources without the need for custom
programming. This enables them to process large health-care data sets an
order of magnitude faster than with legacy technologies.

Joseph Yoder of The Refactory examines strategies, practices, and
patterns drawn from real experiences that support new and evolving
data-processing requirements while keeping the core architecture clean.
As complex systems evolve to meet varying data formats, they can devolve
into poorly architected Big Balls of Mud filled with special-case logic
and one-off processing. Alternatively, you can isolate core components
of your system and protect them from entanglements and unnecessary
complexity by designing them to operate on common data formats while
providing extension mechanisms that enable processing variations.